1. Impacts of spatial resolution on land cover classification Chanida Suwanprasit and Naiyana Srichai Prince of Songkla University Phuket Campus APAN 33 rd Meeting 13-17 February 2012

2. Outline  Introduction  Objective  Methodology  Results  Conclusions 2/20

3. Spatial Resolution is a measurement of the spatial detail in an image, which is a function of the design of the sensor and its operating altitude above the Earth’s surface (Smith, 2012). 3/20 Classification Factors  Number of mixed Pixel  Number of ROIs  Scale or spatial resolution  Spectral resolution  Temporal resolution

4. Spectral reflectance characteristics Source: Smith, 2012 4/20

5. Objective  To examine effects of pixel size on land use classification in Kathu district, Phuket, Thailand 5/20

6. Study area: Kathu, Phuket 7/20 Kathu Kamala Patong

7. Imagery SourceResolution (m)BandSpectral Type LANDSAT 5 TM 301 (Blue) 0.45 – 0.52  m 302 (Green) 0.52 – 0.60  m 303(Red) 0.63 – 0.69  m 304 (NIR) 0.78 – 0.90  m 305 (NIR) 1.55 – 1.75  m 606 (TIR) 10.40 – 12.5  m 307(MIR) 2.80 – 2.35  m THEOS 15 1 (Blue) 0.45 -0.52  m 15 2 (Green) 0.53 – 0.60  m 15 3 (Red) 0.62 – 0.69  m 154 (NIR)0.77 – 0.90  m Data set specification 6/20

8. Source: http://www.hilltophotelpatong.com/booking.html Kathu, Phuket 8/20

9. Band 1 (Blue)Band 2 (Green)Band 3 (Red) Band 4 (NIR) Band 5 (NIR) Band 7 (MIR) Landsat 5 Spectral Bands 10/20

10. Band 1 (Red)Band 2 (Green) Band 3 (Blue) Band 4 (NIR) THEOS Spectral Bands 11/20

11. True Color THEOS Landsat 5 9/20

12. RGB (4,3,2) THEOS Landsat 5 13/20

13. Process Overview THEOS Landsat 5 Classes Forest Built-up Road Water Agriculture Grassland Bare land Classes Forest Built-up Road Water Agriculture Grassland Bare land Unsupervised K-Mean Unsupervised K-Mean Supervised SVMs Supervised SVMs Training area Test area Control points THEOS LandSat 5 Land use Classification Map Data Set 12/20

14. Unsupervised Classification: K-Mean (7 Classes) 14/20

15. ROIs Separability Test Class ForestBuilt-upRoadWaterAgricultureGrasslandBare land Forest-1.9891.9352.0001.7471.9901.973 Built-up1.998-1.1381.9651.9942.0001.483 Road1.9550.658-1.9931.8321.9211.382 Water2.000 - Agriculture1.7761.9731.9422.000-1.6291.961 Grassland1.9831.8551.8362.0001.582-1.985 Bare land1.9991.7431.5672.0001.9941.955- THEOS’s ROIs Landsat’s ROIs 15/20

16. Support Vector Machines : SVMs THEOSLandsat Forest Grassland Bare land Water Built - up Road 16/20

17. Class Confusion Matrix Class THEOSLandsat-5 Prod. Acc. (%) User Acc. (%) Prod. Acc. (%) User Acc. (%) Forest97.4796.81100.00 Built-up62.3771.1897.0297.57 Road74.8964.6290.1590.59 Water99.8799.2983.2578.71 Bare land76.7891.3160.8866.78 Grassland89.4995.2396.0291.85 Agriculture92.2184.2276.6975.37 Overall Accuracy90.65% (Kappa Co.= 0.88)89.00% (Kappa Co.=0.87) 17/20

18. Conclusion  THEOS gave a higher classification accuracy than Landsat 5 for identifying land use in this study.  More Spectral bands from Landsat 5 with 30m is not appropriated for selecting clearly ROIs than THEOS with 15m resolution.  The better resolution image greatly reduce the mixed-pixel problem, and there is the potential to extract much more detailed information on land-use/land cover structures. 18/20

19. References  Duveiller, G. and P. Defourny (2010). "A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing." Remote Sensing of Environment 114(11): 2637-2650.  Randall B. Smith (2012). "Introduction to Remote Sensing Environment (RSE)". Website: http://www.microimages.com. 19/20

20. Acknowledgement  Faculty of Technology and Environment Prince of Songkla University, Phuket Campus  Geo-Informatics and Space Technology Development Agency (Public Organization)  UniNet 20/20

21. Thank you for your kind attention